mtcars[order(mtcars$gear,mtcars$mpg),]

Jaime

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
arrange(.data=mtcars,mtcars$gear,mtcars$mpg)

isaac

Pena

small_mtcars <-
mtcars %>%
arrange(gear) %>%
slice(1:10)
small_mtcars
library(ggplot2)
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
geom_smooth(method = lm)
## `geom_smooth()` using formula 'y ~ x'

#install.packages("pacman")
library(pacman)
## Warning: package 'pacman' was built under R version 4.2.1
p_load(ggplot2, ggthemes, dplyr, readr)
chilean_exports <- "year,product,export,percentage
2006,copper,4335009500,81
2006,others,1016726518,19
2007,copper,9005361914,86
2007,others,1523085299,14
2008,copper,6907056354,80
2008,others,1762684216,20
2009,copper,10529811075,81
2009,others,2464094241,19
2010,copper,14828284450,85
2010,others,2543015596,15
2011,copper,15291679086,82
2011,others,3447972354,18
2012,copper,14630686732,80
2012,others,3583968218,20
2013,copper,15244038840,79
2013,others,4051281128,21
2014,copper,14703374241,78
2014,others,4251484600,22
2015,copper,13155922363,78
2015,others,3667286912,22"
exports_data <- read_csv(chilean_exports)
## Rows: 20 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): product
## dbl (3): year, export, percentage
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
exports_data
p1 <- ggplot(aes(y = export, x = year, colour = product), data = exports_data) +
geom_line()
p1

library(reticulate)
## Warning: package 'reticulate' was built under R version 4.2.1
reticulate::conda_install(packages = "numpy")
## + "C:/Users/jipm1/AppData/Local/r-miniconda/condabin/conda.bat" "install" "--yes" "--name" "r-reticulate" "-c" "conda-forge" "numpy"
import numpy as np
library(readxl)
CEP<-read_excel("D:/CEP_sep-oct_2017.xlsx",sheet=2)
head(CEP)
library(dplyr)
CEP1=select(CEP,pond=POND,sexo=SEXO,
            region=REGION,edad=DS_P2_EXACTA,
           satisfaccion_vida=SV_1,satisfaccion_chilenos=SV_2,eval_econ=MB_P2 )
CEP1
class(CEP1$sexo)
## [1] "numeric"
table(CEP1$sexo)
## 
##   1   2 
## 553 871
library(dplyr)
CEP2<-mutate(CEP1, sexo_chr = dplyr::recode(CEP1$sexo, '1' = "hombre", '2' = "mujer"))
table(CEP2$sexo_chr)
## 
## hombre  mujer 
##    553    871
CEP3 <- mutate(CEP2, sexo_factor = factor(CEP2$sexo, 
                                        labels = c("Hombre", "Mujer")))
class(CEP3$sexo_factor)
## [1] "factor"
table(CEP3$region)
## 
##   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15 
##  24  57  24  52 150  82  94 192  98  69   5  17 501  39  20
library(knitr)
kable(table(CEP3$region))
Var1 Freq
1 24
2 57
3 24
4 52
5 150
6 82
7 94
8 192
9 98
10 69
11 5
12 17
13 501
14 39
15 20
class(CEP3$region)
## [1] "numeric"
library(car)
## Warning: package 'car' was built under R version 4.2.1
## Loading required package: carData
## Warning: package 'carData' was built under R version 4.2.1
## 
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
## 
##     recode
CEP <- mutate(CEP3, region_factor = car::recode(CEP3$region, "1:12 = 1; 13 = 2; 14:15 = 1"))
class(CEP$region_factor)
## [1] "numeric"
library(VIM)
## Warning: package 'VIM' was built under R version 4.2.1
## Loading required package: colorspace
## Loading required package: grid
## VIM is ready to use.
## Suggestions and bug-reports can be submitted at: https://github.com/statistikat/VIM/issues
## 
## Attaching package: 'VIM'
## The following object is masked from 'package:datasets':
## 
##     sleep
library(ggplot2)
head(msleep)
newdata<-kNN(msleep,k=5)
head(newdata)
% Please add the following required packages to your document preamble: % % Note: It may be necessary to compile the document several times to get a multi-page table to line up properly
library(dlookr)      # for exploratory data analysis and imputation
## Warning: package 'dlookr' was built under R version 4.2.1
## 
## Attaching package: 'dlookr'
## The following object is masked from 'package:base':
## 
##     transform
plot_na_pareto(airquality, only_na = TRUE)

plot_na_intersect(airquality)

library(visdat)      # for visualizing NAs
## Warning: package 'visdat' was built under R version 4.2.1
library(plotly)      # for interactive visualization
## Warning: package 'plotly' was built under R version 4.2.1
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
vis_miss(airquality) %>% ggplotly()
## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## Please use `gather()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
blip <- imputate_na(airquality, Ozone, Solar.R, method = "mean")
plot(blip)

blip
##   [1]  41.00000  36.00000  12.00000  18.00000  42.12931  28.00000  23.00000
##   [8]  19.00000   8.00000  42.12931   7.00000  16.00000  11.00000  14.00000
##  [15]  18.00000  14.00000  34.00000   6.00000  30.00000  11.00000   1.00000
##  [22]  11.00000   4.00000  32.00000  42.12931  42.12931  42.12931  23.00000
##  [29]  45.00000 115.00000  37.00000  42.12931  42.12931  42.12931  42.12931
##  [36]  42.12931  42.12931  29.00000  42.12931  71.00000  39.00000  42.12931
##  [43]  42.12931  23.00000  42.12931  42.12931  21.00000  37.00000  20.00000
##  [50]  12.00000  13.00000  42.12931  42.12931  42.12931  42.12931  42.12931
##  [57]  42.12931  42.12931  42.12931  42.12931  42.12931 135.00000  49.00000
##  [64]  32.00000  42.12931  64.00000  40.00000  77.00000  97.00000  97.00000
##  [71]  85.00000  42.12931  10.00000  27.00000  42.12931   7.00000  48.00000
##  [78]  35.00000  61.00000  79.00000  63.00000  16.00000  42.12931  42.12931
##  [85]  80.00000 108.00000  20.00000  52.00000  82.00000  50.00000  64.00000
##  [92]  59.00000  39.00000   9.00000  16.00000  78.00000  35.00000  66.00000
##  [99] 122.00000  89.00000 110.00000  42.12931  42.12931  44.00000  28.00000
## [106]  65.00000  42.12931  22.00000  59.00000  23.00000  31.00000  44.00000
## [113]  21.00000   9.00000  42.12931  45.00000 168.00000  73.00000  42.12931
## [120]  76.00000 118.00000  84.00000  85.00000  96.00000  78.00000  73.00000
## [127]  91.00000  47.00000  32.00000  20.00000  23.00000  21.00000  24.00000
## [134]  44.00000  21.00000  28.00000   9.00000  13.00000  46.00000  18.00000
## [141]  13.00000  24.00000  16.00000  13.00000  23.00000  36.00000   7.00000
## [148]  14.00000  30.00000  42.12931  14.00000  18.00000  20.00000
## attr(,"var_type")
## [1] "numerical"
## attr(,"method")
## [1] "mean"
## attr(,"na_pos")
##  [1]   5  10  25  26  27  32  33  34  35  36  37  39  42  43  45  46  52  53  54
## [20]  55  56  57  58  59  60  61  65  72  75  83  84 102 103 107 115 119 150
## attr(,"type")
## [1] "missing values"
## attr(,"message")
## [1] "complete imputation"
## attr(,"success")
## [1] TRUE
## attr(,"class")
## [1] "imputation" "numeric"